Joint channel and impulsive noise estimation method based on approximate message passing for NOMA systems

LI Youming; MA Chongya; WU Yonghong; GUO Qiang

doi:10.11959/j.issn.1000-0801.2024205

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Joint channel and impulsive noise estimation method based on approximate message passing for NOMA systems

Research and Development | 更新时间：2024-11-25

- Joint channel and impulsive noise estimation method based on approximate message passing for NOMA systems
- Telecommunications Science Vol. 40, Issue 9, Pages: 44-53(2024)
- 作者机构：
  
  1.宁波大学信息科学与工程学院，浙江宁波 315211
  2.中国电波传播研究所，山东青岛 266075
  3.哈尔滨工程大学信息与通信工程学院，黑龙江哈尔滨 150001
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2024205
  CLC： TN929
- Received：10 April 2024，
  
  Revised：2024-08-30，
  
  Published：20 September 2024
- 稿件说明：
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李有明,马冲亚,吴永宏等.基于近似消息传递的NOMA系统信道和脉冲噪声联合估计方法[J].电信科学,2024,40(09):44-53.

LI Youming,MA Chongya,WU Yonghong,et al.Joint channel and impulsive noise estimation method based on approximate message passing for NOMA systems[J].Telecommunications Science,2024,40(09):44-53.
李有明,马冲亚,吴永宏等.基于近似消息传递的NOMA系统信道和脉冲噪声联合估计方法[J].电信科学,2024,40(09):44-53. DOI： 10.11959/j.issn.1000-0801.2024205.

LI Youming,MA Chongya,WU Yonghong,et al.Joint channel and impulsive noise estimation method based on approximate message passing for NOMA systems[J].Telecommunications Science,2024,40(09):44-53. DOI： 10.11959/j.issn.1000-0801.2024205.

摘要

针对非高斯脉冲噪声背景下的非正交多址接入（non-orthogonal multiple access，NOMA）系统的信道估计问题，利用信道和脉冲噪声的稀疏特性，提出一种基于近似消息传递的信道和脉冲噪声联合估计方法。首先构建全子载波的压缩感知方程，然后基于稀疏贝叶斯学习理论提出一种信道、脉冲噪声和数据符号的联合估计优化问题。为解决这一超参量非线性非凸问题，设计了一种基于高斯广义近似消息传递和稀疏贝叶斯学习理论的期望最大化实现算法。仿真结果表明，与基于期望最大化的稀疏贝叶斯学习方法相比，所提算法在信道和脉冲噪声估计的均方误差、误码率等方面性能虽略有下降，但算法复杂度降低了1个数量级。

Abstract

To address the channel estimation problem for non-orthogonal multiple access (NOMA) systems under non-Gaussian impulsive noise

a joint channel and impulsive noise estimation method based on approximate message passing was proposed

by exploiting the joint sparsity of the channel and impulsive noise. Firstly

based on sparse Bayesian learning theory

a compressed sensing equation was constructed by using all subcarriers

and then a joint estimation optimization problem for the channel

impulsive noise

and data symbols was proposed. To address this hyperparameter nonlinear non-convex problem

an expectation maximization (EM) implementation algorithm based on Gaussian generalized approximation message passing and sparse Bayesian learning (SBL) theory was designed. Simulation results show that compared to the SBL method based on EM

the proposed algorithm exhibited a slight degradation in terms of mean square error (MSE) for channel and impulsive noise estimation

bit error rate (BER). However

the complexity of the proposed algorithm was reduced by one order of magnitude.

关键词

Keywords

references

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